Naoyuki Kubota

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Chia-Hsiu Kao [1] Naoyuki Kubota,

a Japanese engineer, researcher in the field of robotics and computational intelligence, and professor at the Tokyo Metropolitan University and head of the Kubota laboratory [2]. He received a Ph.D. in engineering from Nagoya University in 1997. His recent research includes human and smart machine co-learning with the Brain-computer interface, also applied to Go playing.

Photo

Li-Wei Ko, Chang-Shing Lee, Shun-Feng Su, Naoyuki Kubota, and Takenori Obo (back row)

Lu-An Lin playing Go with DDF, and the robot Palro reported real-time suggested next move to Lin

PFML-based BCI Agent

Abtract from PFML-based Semantic BCI Agent for Game of Go Learning and Prediction [4]:

This paper presents a semantic [brain computer interface](https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface) (BCI) agent with [particle swarm optimization](https://en.wikipedia.org/wiki/Particle_swarm_optimization) (PSO) based on a [Fuzzy Markup Language](https://en.wikipedia.org/wiki/Fuzzy_markup_language) (FML) for [Go](Go "Go") [learning](Learning "Learning") and prediction applications.  Additionally, we also establish an Open Go [Darkforest](https://en.wikipedia.org/wiki/Darkforest) (OGD) cloud platform with Facebook AI research (FAIR) open source Darkforest and ELF OpenGo AI bots <a id="cite-note-5" href="#cite-ref-5">[5]</a>. The Japanese robot Palro will simultaneously predict the move advantage in the board game Go to the Go players for reference or learning. The proposed semantic BCI agent operates efficiently by the human-based BCI data from their [brain waves](https://en.wikipedia.org/wiki/Neural_oscillation) and machine-based game data from the prediction of the OGD cloud platform for optimizing the parameters between humans and machines. Experimental results show that the proposed human and smart machine co-learning mechanism performs favorably. We hope to provide students with a better online learning environment, combining different kinds of handheld devices, robots, or computer equipment, to achieve a desired and intellectual learning goal in the future.